# imports
from diffusers import StableDiffusionPipeline
import torch
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
AttributeError: 'MessageFactory' object has no attribute 'GetPrototype'

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
AttributeError: 'MessageFactory' object has no attribute 'GetPrototype'

# Set up Stable Diffusion pipeline
model_id = "runwayml/stable-diffusion-v1-5"
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the pipeline
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if device == "cuda" else torch.float32)
pipe = pipe.to(device)

print(f"Pipeline loaded on {device}")

# Define different prompt styles for testing
prompts = {
    "simple": "A bustling market at dawn with vibrant stalls",
    "abstract": "A whimsical landscape with floating islands and waterfalls",
    "detailed": "A majestic castle on a cliff overlooking a stormy ocean, painted in the style of romantic realism, golden hour lighting, dramatic clouds",
    "artistic": "An ethereal forest spirit dancing among luminescent mushrooms, digital art, fantasy style, soft glowing effects",
    "minimalist": "A single red rose on white background"
}

# Generate images for each prompt style
generated_images = {}
for style, prompt in prompts.items():
    print(f"Generating image for {style} prompt: '{prompt}'")
    
    image = pipe(
        prompt,
        num_inference_steps=20,
        guidance_scale=7.5,
        generator=torch.manual_seed(42)
    ).images[0]
    
    generated_images[style] = image
    
# Display the results
fig, axes = plt.subplots(1, len(prompts), figsize=(20, 4))
for i, (style, image) in enumerate(generated_images.items()):
    axes[i].imshow(image)
    axes[i].set_title(f'{style.capitalize()} Prompt', fontsize=10)
    axes[i].axis('off')

plt.tight_layout()
plt.show()

# Analysis
print("\n📊 Prompt Analysis:")
print("• Simple prompts produce clear, focused images")
print("• Detailed prompts allow for more artistic control")
print("• Abstract prompts encourage creative interpretation")
print("• Artistic style prompts benefit from specific technique mentions")

model_index.json:   0%|          | 0.00/541 [00:00<?, ?B/s]

Fetching 15 files:   0%|          | 0/15 [00:00<?, ?it/s]

preprocessor_config.json:   0%|          | 0.00/342 [00:00<?, ?B/s]

config.json:   0%|          | 0.00/617 [00:00<?, ?B/s]

merges.txt: 0.00B [00:00, ?B/s]

config.json: 0.00B [00:00, ?B/s]

scheduler_config.json:   0%|          | 0.00/308 [00:00<?, ?B/s]

special_tokens_map.json:   0%|          | 0.00/472 [00:00<?, ?B/s]

safety_checker/model.safetensors:   0%|          | 0.00/1.22G [00:00<?, ?B/s]

text_encoder/model.safetensors:   0%|          | 0.00/492M [00:00<?, ?B/s]

# Select a base prompt for parameter exploration
base_prompt = "A serene mountain lake at sunset with misty forests"

# Test different guidance scales
guidance_scales = [5, 7.5, 10, 15, 20]
guidance_images = []

print("🎛️ Testing Guidance Scale Effects:")
for scale in guidance_scales:
    image = pipe(
        base_prompt,
        num_inference_steps=20,
        guidance_scale=scale,
        generator=torch.manual_seed(42)
    ).images[0]
    guidance_images.append(image)
    print(f"Generated image with guidance scale: {scale}")

# Display guidance scale comparison
fig, axes = plt.subplots(1, len(guidance_scales), figsize=(20, 4))
for i, (scale, image) in enumerate(zip(guidance_scales, guidance_images)):
    axes[i].imshow(image)
    axes[i].set_title(f'Guidance: {scale}', fontsize=10)
    axes[i].axis('off')
plt.suptitle('Effect of Guidance Scale on Image Generation')
plt.tight_layout()
plt.show()

# Test different seeds
seeds = [42, 123, 456, 789, 999]
seed_images = []

print("\n🎲 Testing Seed Variations:")
for seed in seeds:
    image = pipe(
        base_prompt,
        num_inference_steps=20,
        guidance_scale=7.5,
        generator=torch.manual_seed(seed)
    ).images[0]
    seed_images.append(image)
    print(f"Generated image with seed: {seed}")

# Display seed comparison
fig, axes = plt.subplots(1, len(seeds), figsize=(20, 4))
for i, (seed, image) in enumerate(zip(seeds, seed_images)):
    axes[i].imshow(image)
    axes[i].set_title(f'Seed: {seed}', fontsize=10)
    axes[i].axis('off')
plt.suptitle('Effect of Different Seeds on Image Generation')
plt.tight_layout()
plt.show()

# Test different step counts
step_counts = [10, 20, 30, 50]
step_images = []

print("\n⏱️ Testing Inference Steps:")
for steps in step_counts:
    image = pipe(
        base_prompt,
        num_inference_steps=steps,
        guidance_scale=7.5,
        generator=torch.manual_seed(42)
    ).images[0]
    step_images.append(image)
    print(f"Generated image with {steps} steps")

# Display steps comparison
fig, axes = plt.subplots(1, len(step_counts), figsize=(16, 4))
for i, (steps, image) in enumerate(zip(step_counts, step_images)):
    axes[i].imshow(image)
    axes[i].set_title(f'{steps} Steps', fontsize=10)
    axes[i].axis('off')
plt.suptitle('Effect of Inference Steps on Image Quality')
plt.tight_layout()
plt.show()

# Parameter analysis
print("\n📈 Parameter Analysis:")
print("• Guidance Scale 5-10: More creative, diverse outputs")
print("• Guidance Scale 15-20: Stricter prompt adherence, less variation")
print("• Different seeds: Significant composition and detail variations")
print("• More steps: Generally improved detail and coherence")

🎛️ Testing Guidance Scale Effects:

  0%|          | 0/20 [00:00<?, ?it/s]

Generated image with guidance scale: 5

  0%|          | 0/20 [00:00<?, ?it/s]

Generated image with guidance scale: 7.5

  0%|          | 0/20 [00:00<?, ?it/s]

Generated image with guidance scale: 10

  0%|          | 0/20 [00:00<?, ?it/s]

Generated image with guidance scale: 15

  0%|          | 0/20 [00:00<?, ?it/s]

# Create a comprehensive portfolio with varied combinations
portfolio_configs = [
    {"prompt": "A cyberpunk cityscape at night", "guidance": 7.5, "steps": 20, "seed": 42},
    {"prompt": "A cyberpunk cityscape at night", "guidance": 15, "steps": 20, "seed": 42},
    {"prompt": "A peaceful zen garden with cherry blossoms", "guidance": 7.5, "steps": 30, "seed": 123},
    {"prompt": "A peaceful zen garden with cherry blossoms", "guidance": 7.5, "steps": 30, "seed": 456},
    {"prompt": "Abstract geometric patterns in vibrant colors", "guidance": 10, "steps": 25, "seed": 789},
    {"prompt": "A majestic dragon flying over mountains", "guidance": 12, "steps": 35, "seed": 999}
]

# Generate portfolio images
portfolio_images = []
portfolio_metadata = []

print("🎨 Generating Portfolio Images:")
for i, config in enumerate(portfolio_configs):
    print(f"Image {i+1}/6: {config['prompt'][:30]}...")
    
    image = pipe(
        config['prompt'],
        num_inference_steps=config['steps'],
        guidance_scale=config['guidance'],
        generator=torch.manual_seed(config['seed'])
    ).images[0]
    
    portfolio_images.append(image)
    portfolio_metadata.append(config)

# Create comprehensive portfolio display
fig, axes = plt.subplots(2, 3, figsize=(18, 12))
axes = axes.flatten()

for i, (image, config) in enumerate(zip(portfolio_images, portfolio_metadata)):
    axes[i].imshow(image)
    title = f"{config['prompt'][:25]}...\nG:{config['guidance']} S:{config['steps']} Seed:{config['seed']}"
    axes[i].set_title(title, fontsize=9)
    axes[i].axis('off')

plt.suptitle('Generated Image Portfolio - Parameter Exploration', fontsize=16)
plt.tight_layout()
plt.show()

# Detailed analysis function
def analyze_image_characteristics(images, configs):
    """Analyze the characteristics of generated images"""
    print("\n🔍 Portfolio Analysis:")
    print("\n1. Prompt Adherence:")
    for i, config in enumerate(configs):
        adherence = "High" if config['guidance'] >= 12 else "Medium" if config['guidance'] >= 8 else "Low"
        print(f"   Image {i+1}: {adherence} adherence (guidance: {config['guidance']})")
    
    print("\n2. Detail Quality:")
    for i, config in enumerate(configs):
        quality = "High" if config['steps'] >= 30 else "Medium" if config['steps'] >= 20 else "Low"
        print(f"   Image {i+1}: {quality} detail (steps: {config['steps']})")
    
    print("\n3. Creative Diversity:")
    prompt_groups = {}
    for i, config in enumerate(configs):
        base_prompt = config['prompt'][:30]
        if base_prompt in prompt_groups:
            prompt_groups[base_prompt].append(i+1)
        else:
            prompt_groups[base_prompt] = [i+1]
    
    for prompt, image_nums in prompt_groups.items():
        if len(image_nums) > 1:
            print(f"   Images {image_nums}: Variations of '{prompt}...'")

# Run analysis
analyze_image_characteristics(portfolio_images, portfolio_metadata)

# Save portfolio images
print("\n💾 Saving Portfolio Images:")
for i, (image, config) in enumerate(zip(portfolio_images, portfolio_metadata)):
    filename = f"portfolio_image_{i+1}_g{config['guidance']}_s{config['steps']}_seed{config['seed']}.png"
    image.save(filename)
    print(f"Saved: {filename}")

# Final recommendations
print("\n🎯 Recommendations for Creative Work:")
print("• For precise brand imagery: Use guidance scale 12-15")
print("• For creative exploration: Use guidance scale 5-8")
print("• For high detail work: Use 30+ inference steps")
print("• For rapid prototyping: Use 15-20 inference steps")
print("• Always test multiple seeds for prompt variations")

🎨 Generating Portfolio Images:
Image 1/6: A cyberpunk cityscape at night...

  0%|          | 0/20 [00:00<?, ?it/s]

Image 2/6: A cyberpunk cityscape at night...

  0%|          | 0/20 [00:00<?, ?it/s]

Image 3/6: A peaceful zen garden with che...

  0%|          | 0/30 [00:00<?, ?it/s]

Image 4/6: A peaceful zen garden with che...

  0%|          | 0/30 [00:00<?, ?it/s]

Image 5/6: Abstract geometric patterns in...

  0%|          | 0/25 [00:00<?, ?it/s]

# Set up Stable Diffusion pipeline
model_id = "runwayml/stable-diffusion-v1-5"
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the pipeline
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if device == "cuda" else torch.float32)
pipe = pipe.to(device)

print(f"Pipeline loaded on {device}")

# Define different prompt styles for testing
prompts = {
    "simple": "A bustling market at dawn with vibrant stalls",
    "abstract": "A whimsical landscape with floating islands and waterfalls",
    "detailed": "A majestic castle on a cliff overlooking a stormy ocean, painted in the style of romantic realism, golden hour lighting, dramatic clouds",
    "artistic": "An ethereal forest spirit dancing among luminescent mushrooms, digital art, fantasy style, soft glowing effects",
    "minimalist": "A single red rose on white background"
}

# Generate images for each prompt style
generated_images = {}
for style, prompt in prompts.items():
    print(f"Generating image for {style} prompt: '{prompt}'")
    
    image = pipe(
        prompt,
        num_inference_steps=20,
        guidance_scale=7.5,
        generator=torch.manual_seed(42)
    ).images[0]
    
    generated_images[style] = image
    
# Display the results
fig, axes = plt.subplots(1, len(prompts), figsize=(20, 4))
for i, (style, image) in enumerate(generated_images.items()):
    axes[i].imshow(image)
    axes[i].set_title(f'{style.capitalize()} Prompt', fontsize=10)
    axes[i].axis('off')

plt.tight_layout()
plt.show()

# Analysis
print("\n📊 Prompt Analysis:")
print("• Simple prompts produce clear, focused images")
print("• Detailed prompts allow for more artistic control")
print("• Abstract prompts encourage creative interpretation")
print("• Artistic style prompts benefit from specific technique mentions")

🤖💻 Generate Custom Images with Stable Diffusion¶

📋 Overview¶

🎯 Learning Outcomes¶

📖 Scenario¶

Task 1: Evaluate Different Prompts [20 minutes]¶

Task 2: Parameter Exploration [25 minutes]¶

Task 3: Comparative Analysis [15 minutes]¶

💻 Exemplar Solution¶

Task 1 Solution¶

Task 2 Solution¶

Task 3 Solution¶